## Step 1: Define the symbolic representation of the variables
The variables are 'manila envelopes', 'red highlighters', and 'paper clips'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9x_1 + 8x_2 + 1x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $9.78x_1 + 12.61x_3 \geq 40$
- $9.78x_1 + 4.69x_2 \geq 62$
- $9.78x_1 + 4.69x_2 + 12.61x_3 \geq 62$
- $2x_1 - 7x_2 \geq 0$
- $x_1$ is an integer
- $x_2$ is an integer
- $x_3$ is an integer

## 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'manila envelopes'), ('x2', 'red highlighters'), ('x3', 'paper clips')],
    'objective_function': '9*x1 + 8*x2 + 1*x3',
    'constraints': [
        '9.78*x1 + 12.61*x3 >= 40',
        '9.78*x1 + 4.69*x2 >= 62',
        '9.78*x1 + 4.69*x2 + 12.61*x3 >= 62',
        '2*x1 - 7*x2 >= 0',
        'x1 is an integer',
        'x2 is an integer',
        'x3 is an integer'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name='manila_envelopes', vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='red_highlighters', vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name='paper_clips', vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(9 * x1 + 8 * x2 + x3, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(9.78 * x1 + 12.61 * x3 >= 40)
    model.addConstr(9.78 * x1 + 4.69 * x2 >= 62)
    model.addConstr(9.78 * x1 + 4.69 * x2 + 12.61 * x3 >= 62)
    model.addConstr(2 * x1 - 7 * x2 >= 0)

    # Set the sustainability score upper bound (not directly mentioned as a constraint but as an attribute)
    # This is not a constraint but an attribute, so we don't add it directly to the model

    # Solve the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Manila envelopes: {x1.varValue}")
        print(f"Red highlighters: {x2.varValue}")
        print(f"Paper clips: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```